Seismology, which is a sub-branch of geophysics, is one of the fields in which data
mining methods can be effectively applied. In this chapter, employing data mining
techniques on multivariate seismic data, decomposition of non-spatial variable is
done. Then k-means clustering, density-based spatial clustering of applications
with noise (DBSCAN), and hierarchical tree clustering algorithms are applied on
decomposed data, and then pattern analysis is conducted using spatial data on the
resulted clusters. The conducted analysis suggests that the clustering results with
spatial data is compatible with the reality and characteristic features of regions
related to earthquakes can be determined as a result of modeling seismic data using
clustering algorithms. The baseline metric reported is clustering times for varying
size of inputs.
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